ApInAPDB: a database of apoptosis-inducing anticancer peptides

ApInAPDB (Apoptosis-Inducing Anticancer Peptides Database) consists of 818 apoptosis-inducing anticancer peptides which are manually collected from research articles. The database provides scholars with peptide related information such as function, binding target and affinity, IC50 and etc. In addition, GRAVY (grand average of hydropathy), net charge at pH 7, hydrophobicity and other physicochemical properties are calculated and presented. Another category of information are structural information includes 3D modeling, secondary structure prediction and descriptors for QSAR (quantitative structure–activity relationship) modeling. In order to facilitate the browsing process, three types of user-friendly searching tools are provided: top categories browser, simple search and advanced search. Overall ApInAPDB as the first database presenting apoptosis-inducing anticancer peptides can be useful in the field of peptide design and especially cancer therapy. Researchers can freely access the database at http://bioinf.modares.ac.ir/software/ApInAPDB/.

www.nature.com/scientificreports/ peptide and also different algorithms used for structural prediction will be discussed in the database information section. The overall ApInAPDB structure with its features is shown in Fig. 1. This database has the potential to aid in the development and design of novel apoptosis-inducing peptides.

Results
The ApInAPDB interface (Fig. 2) has been designed for searching and browsing the database conveniently.
Instructions on how to search and browse in this user-friendly interface are given below.
Searching tool. Simple search. Through a simple search box, keywords can be searched on peptide name, cell line, whole/partial peptide sequence or binding target. Here the keyword search for each field is done separately.
Advanced search. This search allows the users to select more than one field at a time. For some fields such as charge of peptide, a range can be defined and for some fields such as cell line, more than one option can be selected. Consequently, users can find peptides with multiple desired properties. Among these properties, secondary structure is more complex and users can confine the searching via parameters including Min/Max content of Helix, Extended, Turn and random coil peptides. An example of result page is displayed in Fig. 3.
Top categories browser. The top categories browser was developed to find information based on five major categories including length of peptide sequence, peptide charge, source, binding target and finally secondary structure. Through this, users can browse the database more easily. As length, charge and secondary structure play crucial roles in peptide efficiency and knowing their source and binding target is critical information, users can browse and compare the different peptides based on these categories and gain more insight into peptide design. A schematic representation of distribution of apoptosis-inducing Anticancer Peptides based on length, binding target, source and charge is shown in Fig. 4.  www.nature.com/scientificreports/ Also, using a de novo approach with the PEP-FOLD3 server, peptide structures were predicted. Some peptides were not considered in this modeling because of limitations with PEP-FOLD3 including peptide size (between 5 and 50 residues) and amino acid types. In total, the structures of 765 peptides were predicted and then compiled in the database. In addition, descriptors of 777 peptides were calculated using Biotriangle web server for QSAR (quantitative structure-activity relationship) modeling development. Other peptides were excluded due to amino acid type's limitation of the web server.

Discussion
Peptides possess considerable potential in cancer therapy due to their target specificity. Several peptides have recently been approved for cancer treatment, including leuprolide, Buserelin, Gonadorelin, Cetrorelix, Abarelix, Degarelix, and Octreotide. In addition, several anticancer peptides are in or about to enter clinical trials, www.nature.com/scientificreports/ including BT1718, DPT-C9h, p28 10,17 . Since deregulation of apoptosis is a feature of many cancers, drugs including peptides which induce normal apoptosis pathway are potential candidates for treating cancers efficiently 18 , Thus, there is great interest in designing apoptosis-inducing peptides for cancer therapy, and abundance of literature is available in this area 19,20 . There are several databases which collected peptides and proteins used in cancer therapy researches like CancerPPD 4 , TumorHope 21 (tumor homing peptides database), dbPepNeo 22  www.nature.com/scientificreports/ (human tumor neoantigen peptides database) and ApoCanD 23 (human apoptotic proteins database on cancer) but there is not any database which present specifically apoptosis-inducing peptides thus this study aimed to develop ApInAPDB to cover this gap. ApInAPDB attempts to present information for researchers interested in studying and developing apoptosis inducing peptides in order to: (1) find the most efficient peptide, (2) determine if designed peptides are novel, (3) perform docking, molecular dynamic simulation and screening for peptides with known binding target reported for each peptide (4) develop QSAR models for those analogues with the same lead peptide/ protein using calculated descriptor files (5) design apoptosis-inducing peptides focusing on the secondary structure prediction, (6) find comprehensive physicochemical properties as well as original information, and (7) browse database with a user-friendly searching tool.

Methods
Data collection. This study aimed to establish a database including anticancer peptides with apoptosis activity regardless of their degree of effectiveness. To cover this aim, collecting and investigating a great number of research articles and available databases such as DRAMP 24 (antimicrobial peptides database) are required. The most relevant research articles were manually gathered from Scopus, Google Scholar, and PubMed using keywords such as 'apoptotic peptides' , 'BCL2 family peptides' , 'apoptosis-inducing peptides' , 'BH3 mimicking peptides' , 'anticancer peptides' and their combination. The collected articles were carefully studied by the authors to select those that reported the apoptosis-inducing property for their investigated peptide. Accordingly, the irrelevant articles were excluded and relevant articles that experimentally recommended the apoptosis-inducing peptides or innately apoptotic peptides like Bim (BCL-2 interacting mediator of cell death) analogues were included in the database. Totally 818 apoptosis-inducing peptides were presented in the ApInAPDB. Out of them, 783 peptides were collected from research articles and 35 peptides were collected from DRAMP database. Database information. Three categories of information are stored in ApInAPDB as original, physicochemical properties and structural information. The original information gathered from research articles includes peptides name, source, sequence, sequence length, N-terminal modification, C-terminal modification, binding target, L/D/Mix, presence of non-natural amino acid, linear/cyclic, function, journal name, publication year, DOI, authors, the title of the paper, cell lines, IC50 of peptide and binding affinity. The next category is physicochemical properties which are derived from the original information. Since peptide properties such as isoelectric point, molecular weight, net charge, etc., play an important role in cancer cell proliferation inhibition or eradication 25 , they are reported in the database including molecular weight (g/mol), amino acid composition, hydrophobic/acidic/neutral/basic amino acid percentage, net charge at pH 7, isoelectric point, hydrophobicity at pH 2.0, hydrophobicity at pH 6.8 and charge using biosyn peptide property calculator v3.1 online tool (https:// www. biosyn. com). Also, GRAVY (grand average of hydropathy) of each peptide is reported in the database using novoprolabs peptide property calculator online tool (https:// www. novop rolabs. com/ tools/ calc_ pepti de_ prope rty). The third and last category is structural information including descriptors for QSAR modeling, secondary structure prediction and 3D modeling. To help scientists in QSAR modeling development, 1559 descriptors are calculated for each peptide using BioProt available in Biotriangle web server (http:// biotr iangle. scbdd. com/ prote in/ index/) 26 and presented as downloadable CSV file format (The exemplary hyperlink belongs to ZXR-1 peptide). As peptide secondary structure plays an important role in binding to the target, secondary structure prediction is reported in ApInAPDB database using GOR (Garnier, Osguthorpe and Robson method), Neural Network and Chou-Fasman algorithms (http:// cib. cf. ocha. ac. jp/ bitool/ MIX/) (ver. 1.1) available on Center for Information Biology (CIB) created by Ochanomizu University. Furthermore, CIB provides users with the facility to make a consensus on the secondary structure prediction according to the three aforementioned algorithms. Using this ability, a consentaneous result is reported for each peptide. Additionally, using PEP-FOLD3 server (https:// biose rv. rpbs. univ-paris-dider ot. fr/ servi ces/ PEP-FOLD3/) 27 both Local Structure prediction profile and downloadable PDB structure file are available (The exemplary hyperlink belongs to ZXR-1 peptide).

Conclusions
Nowadays, apoptosis-inducing peptides play an important role in cancer therapy and they attract the attention of researchers in the field of peptide design and synthesis. This fact calls for a comprehensive database that includes this type of peptide as a reference for scholars. ApInAPDB is a database that consists of more than 800 apoptosis-inducing anticancer peptides collected from different articles and it provides comprehensive and useful information about each peptide. The information includes physicochemical properties, secondary structure prediction and descriptors for QSAR modeling among others. Since updating the database with novel information is very important, authors will attempt to update it and add newly studied apoptosis-inducing peptides with their original, physicochemical properties and structural information. Hence, ApInAPDB is certainly helpful for researchers willing to design apoptosis-inducing peptides for cancer therapy.